<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In order to improve the ability of power communication technology to support large-scale and massive services of smart electricity consumption, this paper designs a sensing model to collect information on new energy electricity consumption. Using the arrival rate calculation method of intelligent IoT, the information on new energy electricity users is collected, and the environmental measurement data is collected with the help of intelligent sensors. Monitoring the concurrency factor of all sensors in the operation state, collecting queuing theory packet loss rate through utilization maximization objective function, and setting the average queuing length of communication nodes. On this basis, the voltage amplitude of the electrical energy sampling point is measured, and the Gaussian perceptual loss vector with non-zero mean and heteroskedasticity is performed to iteratively converge the constraint matrix and error covariance. The results show that the maximum relative errors for service delay and packet loss rate are 16.8% and 7.0%, respectively. As bandwidth increases, the queuing delay and packet loss rate decrease. The broadband power line carrier can reach up to 210 Mbps, and the micropower wireless rate can reach 38.5 kbps. The annual cycle characteristics fluctuate widely, but the cumulative error of electricity load prediction results is lower, and the prediction accuracy is high. It shows that the electricity consumption information collection sensing model provides an important reference for building a new communication network with low cost, high speed and reliability for smart electricity consumption.</p>
</abstract>

In order to improve the ability of power communication technology to support large-scale and massive services of smart electricity consumption, this paper designs a sensing model to collect information on new energy electricity consumption. Using the arrival rate calculation method of intelligent IoT, the information on new energy electricity users is collected, and the environmental measurement data is collected with the help of intelligent sensors. Monitoring the concurrency factor of all sensors in the operation state, collecting queuing theory packet loss rate through utilization maximization objective function, and setting the average queuing length of communication nodes. On this basis, the voltage amplitude of the electrical energy sampling point is measured, and the Gaussian perceptual loss vector with non-zero mean and heteroskedasticity is performed to iteratively converge the constraint matrix and error covariance. The results show that the maximum relative errors for service delay and packet loss rate are 16.8% and 7.0%, respectively. As bandwidth increases, the queuing delay and packet loss rate decrease. The broadband power line carrier can reach up to 210 Mbps, and the micropower wireless rate can reach 38.5 kbps. The annual cycle characteristics fluctuate widely, but the cumulative error of electricity load prediction results is lower, and the prediction accuracy is high. It shows that the electricity consumption information collection sensing model provides an important reference for building a new communication network with low cost, high speed and reliability for smart electricity consumption.

Research on the development of AloT sensing module for new energy power information collection

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In order to improve the ability of power communication technology to support large-scale and massive services of smart electricity consumption, this paper...

Research on the development of AloT sensing module for new energy power information collection

Pubblicato online: 25 nov 2023

Pagine: -

Ricevuto: 26 feb 2023

Accettato: 05 giu 2023

DOI: https://doi.org/10.2478/amns.2023.2.01242

Parole chiave
Electricity consumption information acquisition, Smart sensors, Electric energy voltage amplitude, Gaussian sensing loss, Constraint matrix

© 2023 Zhen Jing et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the development of AloT sensing module for new energy power information collection

Pubblicato online: 25 nov 2023

Pagine: -

Ricevuto: 26 feb 2023

Accettato: 05 giu 2023

DOI: https://doi.org/10.2478/amns.2023.2.01242

Parole chiaveElectricity consumption information acquisition, Smart sensors, Electric energy voltage amplitude, Gaussian sensing loss, Constraint matrix

© 2023 Zhen Jing et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Electricity consumption information acquisition, Smart sensors, Electric energy voltage amplitude, Gaussian sensing loss, Constraint matrix